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MMRec: Simplifying Multimodal Recommendation

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arxiv 2302.03497 v2 pith:6EVEXNAY submitted 2023-02-02 cs.IR cs.MM

Xin Zhou This is my paper · ORCID
classification cs.IRcs.MM
keywords mmrecmultimodalrecommendationmodelsimplementingalgorithmsarenaavailable
verification ladder T0 review T1 audit T2 compute T3 formal

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This paper presents an open-source toolbox, MMRec for multimodal recommendation. MMRec simplifies and canonicalizes the process of implementing and comparing multimodal recommendation models. The objective of MMRec is to provide a unified and configurable arena that can minimize the effort in implementing and testing multimodal recommendation models. It enables multimodal models, ranging from traditional matrix factorization to modern graph-based algorithms, capable of fusing information from multiple modalities simultaneously. Our documentation, examples, and source code are available at \url{https://github.com/enoche/MMRec}.

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Forward citations

Cited by 6 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. One Graph, Multiple Gains: Single High-Quality Item-Item Graph for Multimodal Recommendation

    cs.IR 2026-07 conditional novelty 6.0 of 10

    A single NCER-refined item-item graph, reused via adaptive gating, UI expansion, and discounted soft-positive BPR, improves multimodal recommendation accuracy and efficiency.

  2. The Best is Yet to Come: Graph Convolution in the Testing Phase for Multimodal Recommendation

    cs.IR 2025-07 conditional novelty 6.0 of 10

    A multimodal recommender that trains without graph convolution and applies it only at test time outperforms graph-trained baselines while training much faster.

  3. STAIR: Manipulating Collaborative and Multimodal Information for E-Commerce Recommendation

    cs.IR 2024-12 conditional novelty 6.0 of 10

    STAIR combines collaborative and multimodal signals in e-commerce recommendation using a dimension-wise stepwise graph convolution and a backward constrained update, outperforming prior methods by 2 to 6 percent.

  4. MDVT: Enhancing Multimodal Recommendation with Model-Agnostic Multimodal-Driven Virtual Triplets

    cs.IR 2025-05 conditional novelty 5.0 of 10

    MDVT improves multimodal recommenders by training them on virtual positive/negative item pairs selected by embedding similarity after a warm-up phase.

  5. Collaborative Filtering Meets Spectrum Shift: Connecting User-Item Interaction with Graph-Structured Side Information

    cs.IR 2025-02 conditional novelty 5.0 of 10

    Spectrum Shift Correction applies an affine transform to the augmented adjacency matrix to restore the [-1,1] spectral range assumed by LightGCN and JGCF, improving social and multimodal recommendation.

  6. Beyond Graph Convolution: Multimodal Recommendation with Topology-aware MLPs

    cs.IR 2024-12 conditional novelty 5.0 of 10

    TMLP models multimodal item relations with MLPs plus topological pruning and mutual-information learning, outperforming GCN-based recommenders on three Amazon datasets.

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